| Model Type | | text classification, sentiment analysis |
|
| Use Cases |
| Areas: | | Customer feedback analysis, Social media monitoring |
|
| Applications: | | E-commerce review analysis, Film industry sentiment tracking |
|
| Primary Use Cases: | | Sentiment analysis for product reviews, Automated social media sentiment responses |
|
| Limitations: | | Not effective for sarcasm, Limited non-English language support |
|
| Considerations: | | Punctuation and context complexity can affect accuracy. |
|
|
| Additional Notes | | Integration examples provided for web applications. |
|
| Supported Languages | | English (Fluent), Spanish (Moderate) |
|
| Training Details |
| Data Sources: | | Amazon reviews, Yelp reviews, IMDB reviews |
|
| Data Volume: | |
| Methodology: | | Fine-tuned with labeled sentiment data |
|
| Context Length: | |
| Training Time: | |
| Hardware Used: | |
| Model Architecture: | |
|
| Safety Evaluation |
| Methodologies: | | Adversarial testing, Bias testing |
|
| Findings: | | Handles explicit language neutrally, Occasionally polarized in political contexts |
|
| Risk Categories: | |
| Ethical Considerations: | | Ensure diverse dataset representation to reduce bias. |
|
|
| Responsible Ai Considerations |
| Fairness: | | Model outputs should be checked for bias, particularly in culturally sensitive contexts. |
|
| Transparency: | | Implement interpretability features for sentiment predictions. |
|
| Accountability: | | Developers are responsible for auditing and maintaining model output accuracy. |
|
| Mitigation Strategies: | | Regular audits and updates with diverse datasets. |
|
|
| Input Output |
| Input Format: | | Prompts should be prefixed with '[Q]' and use curly braces for input. |
|
| Accepted Modalities: | |
| Output Format: | | Structured JSON with sentiment classification and confidence score. |
|
| Performance Tips: | | Provide clear and contextually balanced sentences for improved accuracy. |
|
|
| Release Notes |
| Version: | |
| Date: | |
| Notes: | | Initial release of the fine-tuned sentiment model. |
|
| Version: | |
| Date: | |
| Notes: | | Improved handling of ambiguous language cases. |
|
|
|
